Decision Properties of Regular Languages General Discussion of “Properties” The Pumping Lemma Membership, Emptiness, Etc. 1
Properties of Language Classes A language class is a set of languages. We have one example: the regular languages. We’ll see many more in this class. Language classes have two important kinds of properties: 1. Decision properties. 2. Closure properties. 2
Representation of Languages Representations can be formal or informal. Example (formal): represent a language by a RE or DFA defining it. Example: (informal): a logical or prose statement about its strings: { 0 n 1 n | n is a nonnegative integer} “The set of strings consisting of some number of 0’s followed by the same number of 1’s.” 3
Decision Properties A decision property for a class of languages is an algorithm that takes a formal description of a language (e.g., a DFA) and tells whether or not some property holds. Example: Is language L empty? 4
Subtle Point: Representation Matters You might imagine that the language is described informally, so if my description is “the empty language” then yes, otherwise no. But the representation is a DFA (or a RE that you will convert to a DFA). Can you tell if L(A) = for DFA A? 5
Why Decision Properties? When we talked about protocols represented as DFA’s, we noted that important properties of a good protocol were related to the language of the DFA. Example: “Does the protocol terminate?” = “Is the language finite?” Example: “Can the protocol fail?” = “Is the language nonempty?” 6
Why Decision Properties – (2) We might want a “smallest” representation for a language, e.g., a minimum-state DFA or a shortest RE. If you can’t decide “Are these two languages the same?” I.e., do two DFA’s define the same language? You can’t find a “smallest.” 7
Closure Properties A closure property of a language class says that given languages in the class, an operator (e.g., union) produces another language in the same class. Example: the regular languages are obviously closed under union, concatenation, and (Kleene) closure. Use the RE representation of languages. 8
Why Closure Properties? 1. Helps construct representations. 2. Helps show (informally described) languages not to be in the class. 9
Example: Use of Closure Property We can easily prove L 1 = { 0 n 1 n | n > 0} is not a regular language. L 2 = the set of strings with an = number of 0’s and 1’s isn’t either, but that fact is trickier to prove. Regular languages are closed under . If L 2 were regular, then L 2 L( 0 * 1 * ) = L 1 would be, but it isn’t. 10
The Membership Question Our first decision property is the question: “is string w in regular language L?” Assume L is represented by a DFA A. Simulate the action of A on the sequence of input symbols forming w. 11
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 12
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 13
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 14
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 15
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 16
Example: Testing Membership 0 1 0 1 1 Next symbol 0 0,1 1 1 A B C Start 0 Current state 17
What if the Regular Language Is not Represented by a DFA? There is a circle of conversions from one form to another: RE ε -NFA DFA NFA 18
The Emptiness Problem Given a regular language, does the language contain any string at all. Assume representation is DFA. Construct the transition graph. Compute the set of states reachable from the start state. If any final state is reachable, then yes, else no. 19
The Infiniteness Problem Is a given regular language infinite? Start with a DFA for the language. Key idea: if the DFA has n states, and the language contains any string of length n or more, then the language is infinite. Otherwise, the language is surely finite. Limited to strings of length n or less. 20
Proof of Key Idea If an n-state DFA accepts a string w of length n or more, then there must be a state that appears twice on the path labeled w from the start state to a final state. Because there are at least n+ 1 states along the path. 21
Proof – (2) w = xyz y x z Then xy i z is in the language for all i > 0. Since y is not ε , we see an infinite number of strings in L. 22
Infiniteness – Continued We do not yet have an algorithm. There are an infinite number of strings of length > n, and we can’t test them all. Second key idea: if there is a string of length > n (= number of states) in L, then there is a string of length between n and 2n-1. 23
Proof of 2 nd Key Idea y Remember: x z We can choose y to be the first cycle on the path. So |xy| < n; in particular, 1 < |y| < n. Thus, if w is of length 2n or more, there is a shorter string in L that is still of length at least n. Keep shortening to reach [n, 2n-1]. 24
Completion of Infiniteness Algorithm Test for membership all strings of length between n and 2n-1. If any are accepted, then infinite, else finite. A terrible algorithm. Better: find cycles between the start state and a final state. 25
Finding Cycles 1. Eliminate states not reachable from the start state. 2. Eliminate states that do not reach a final state. 3. Test if the remaining transition graph has any cycles. 26
The Pumping Lemma We have, almost accidentally, proved a statement that is quite useful for showing certain languages are not regular. Called the pumping lemma for regular languages . 27
Statement of the Pumping Lemma Number of For every regular language L states of DFA for L There is an integer n, such that For every string w in L of length > n We can write w = xyz such that: 1. |xy| < n. Labels along 2. |y| > 0. first cycle on path labeled w 3. For all i > 0, xy i z is in L. 28
Example: Use of Pumping Lemma We have claimed { 0 k 1 k | k > 1} is not a regular language. Suppose it were. Then there would be an associated n for the pumping lemma. Let w = 0 n 1 n . We can write w = xyz, where x and y consist of 0’s, and y ε . But then xyyz would be in L, and this string has more 0’s than 1’s. 29
Decision Property: Equivalence Given regular languages L and M, is L = M? Algorithm involves constructing the product DFA from DFA’s for L and M. Let these DFA’s have sets of states Q and R, respectively. Product DFA has set of states Q R. I.e., pairs [q, r] with q in Q, r in R. 30
Product DFA – Continued Start state = [q 0 , r 0 ] (the start states of the DFA’s for L, M). Transitions: δ ([q,r], a) = [ δ L (q,a), δ M (r,a)] δ L , δ M are the transition functions for the DFA’s of L, M. That is, we simulate the two DFA’s in the two state components of the product DFA. 31
Example: Product DFA 0 0 1 0 A B [A,C] [A,D] 1 0, 1 1 1 0 0 1 [B,C] [B,D] 0 1 0 C D 1 32
Equivalence Algorithm Make the final states of the product DFA be those states [q, r] such that exactly one of q and r is a final state of its own DFA. Thus, the product accepts w iff w is in exactly one of L and M. 33
Example: Equivalence 0 0 1 0 A B [A,C] [A,D] 1 0, 1 1 1 0 0 1 [B,C] [B,D] 0 1 0 C D 1 34
Equivalence Algorithm – (2) The product DFA’s language is empty iff L = M. But we already have an algorithm to test whether the language of a DFA is empty. 35
Decision Property: Containment Given regular languages L and M, is L M? Algorithm also uses the product automaton. How do you define the final states [q, r] of the product so its language is empty iff L M? Answer: q is final; r is not. 36
Example: Containment 0 0 1 0 A B [A,C] [A,D] 1 0, 1 1 1 0 0 1 [B,C] [B,D] 0 1 0 C D Note: the only final state 1 is unreachable, so containment holds. 37
The Minimum-State DFA for a Regular Language In principle, since we can test for equivalence of DFA’s we can, given a DFA A find the DFA with the fewest states accepting L(A). Test all smaller DFA’s for equivalence with A . But that’s a terrible algorithm. 38
Efficient State Minimization Construct a table with all pairs of states. If you find a string that distinguishes two states (takes exactly one to an accepting state), mark that pair. Algorithm is a recursion on the length of the shortest distinguishing string. 39
State Minimization – (2) Basis: Mark a pair if exactly one is a final state. Induction: mark [q, r] if there is some input symbol a such that [ δ (q,a), δ (r,a)] is marked. After no more marks are possible, the unmarked pairs are equivalent and can be merged into one state. 40
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